Sequor

Sequor is a sequence labeler based on Collins's (2002)
perceptron. Sequor has a flexible feature template language and is
meant mainly for NLP applications such as Named Entity labeling, Part
of Speech tagging or syntactic chunking. It includes the SemiNER named
entity recognizer, with pre-trained models for German and English (see
Named Entity Recognition (SemiNER)).

Sequor is especially useful if your dataset has a large label set. In
this case it is likely to run faster and allow you to use much less
RAM than a sequence labeler based on Conditional Random
Fields. Additionally sequor implements options which allow you to
control the size of model and tradeoff speed against accuracy:

Installation

Cabal should then download and install the necessary packages, and
install the sequor binary in ./bin, and the data files in ./share

Usage

With Sequor you can learn a model from sequences manually annotated
with labels, and then apply this model to new data in order to add
labels. Sequor is meant to be used mainly with linguistic data, for
example to learn Part of Speech tagging, syntactic chunking or Named
Entity labeling: